Predictive Models Aid Prognostication
Bibliographic record
Abstract
BACKGROUND: In a recent multicenter Canadian study in heart failure (HF), model predictions proved more accurate than physicians. OBJECTIVES: Simulating clinical practice, the authors evaluated the predictive value of combining model predictions with physician estimated 1-year mortality in HF outpatients. METHODS: This post hoc analysis of a Canadian multicenter cohort study included HF outpatients (left ventricular ejection fraction ≤40%). HF cardiologists and family doctors estimated patient 1-year mortality using clinical judgment. The Seattle HF Model (SHFM) predicted mortality. All patients were followed for 1 year to collect mortality. Stratified by specialty, we compared the performance of SHFM and physician estimates alone, with a model integrating physician and SHFM predictions using a random forest survival model, evaluating discrimination (C-statistic), calibration (observed vs predicted event rate), risk reclassification, and clinical net benefit. RESULTS: In 1,643 HF patients, 1-year mortality was 9% (95% CI: 8%-11%). The SHFM had adequate discrimination (C-statistic 0.76; 95% CI: 0.72-0.80) and excellent calibration. Physicians showed adequate discrimination (0.75; 95% CI: 0.71-0.79 for cardiologists; 0.72; 95% CI: 0.66-0.78 for family doctors) and poor calibration with significant risk overestimation. Integrating SHFM and physician predictions, discrimination significantly improved (0.82; 95% CI: 0.78-0.86 for cardiologists; 0.87; 95% CI: 0.83-0.91 for family doctors) with excellent calibration. By risk reclassification, among patients without events, the integrated model better risk-classified 71% (95% CI: 70%-72%) vs cardiologists and 60% (95% CI: 58%-61%) vs family doctors; among patients with events, the model misclassified 45% (95% CI: 58%-63%) vs cardiologists and 11% (95% CI: 25% to 3%) vs family doctors. The integrated model led to higher clinical benefit. CONCLUSIONS: Integrating SHFM predictions with physician judgment improved accuracy. Model-informed assessment provides prognostic accuracy for clinical decision-making. (Predicted Prognosis in Heart Failure Intuition; NCT04009798).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".